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  <div class="section" id="numpy-nanstd">
<h1>numpy.nanstd<a class="headerlink" href="#numpy-nanstd" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.nanstd">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">nanstd</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">ddof=0</em>, <em class="sig-param">keepdims=&lt;no value&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/nanfunctions.py#L1571-L1672"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.nanstd" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the standard deviation along the specified axis, while
ignoring NaNs.</p>
<p>Returns the standard deviation, a measure of the spread of a
distribution, of the non-NaN array elements. The standard deviation is
computed for the flattened array by default, otherwise over the
specified axis.</p>
<p>For all-NaN slices or slices with zero degrees of freedom, NaN is
returned and a <em class="xref py py-obj">RuntimeWarning</em> is raised.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.8.0.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Calculate the standard deviation of the non-NaN values.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">{int, tuple of int, None}, optional</span></dt><dd><p>Axis or axes along which the standard deviation is computed. The default is
to compute the standard deviation of the flattened array.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">dtype, optional</span></dt><dd><p>Type to use in computing the standard deviation. For arrays of
integer type the default is float64, for arrays of float types it
is the same as the array type.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternative output array in which to place the result. It must have
the same shape as the expected output but the type (of the
calculated values) will be cast if necessary.</p>
</dd>
<dt><strong>ddof</strong><span class="classifier">int, optional</span></dt><dd><p>Means Delta Degrees of Freedom.  The divisor used in calculations
is <code class="docutils literal notranslate"><span class="pre">N</span> <span class="pre">-</span> <span class="pre">ddof</span></code>, where <code class="docutils literal notranslate"><span class="pre">N</span></code> represents the number of non-NaN
elements.  By default <em class="xref py py-obj">ddof</em> is zero.</p>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original <em class="xref py py-obj">a</em>.</p>
<p>If this value is anything but the default it is passed through
as-is to the relevant functions of the sub-classes.  If these
functions do not have a <em class="xref py py-obj">keepdims</em> kwarg, a RuntimeError will
be raised.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>standard_deviation</strong><span class="classifier">ndarray, see dtype parameter above.</span></dt><dd><p>If <em class="xref py py-obj">out</em> is None, return a new array containing the standard
deviation, otherwise return a reference to the output array. If
ddof is &gt;= the number of non-NaN elements in a slice or the slice
contains only NaNs, then the result for that slice is NaN.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="numpy.var.html#numpy.var" title="numpy.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a>, <a class="reference internal" href="numpy.mean.html#numpy.mean" title="numpy.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a>, <a class="reference internal" href="numpy.std.html#numpy.std" title="numpy.std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">std</span></code></a>, <a class="reference internal" href="numpy.nanvar.html#numpy.nanvar" title="numpy.nanvar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanvar</span></code></a>, <a class="reference internal" href="numpy.nanmean.html#numpy.nanmean" title="numpy.nanmean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmean</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">ufuncs-output-type</span></code></p>
</div>
<p class="rubric">Notes</p>
<p>The standard deviation is the square root of the average of the squared
deviations from the mean: <code class="docutils literal notranslate"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">sqrt(mean(abs(x</span> <span class="pre">-</span> <span class="pre">x.mean())**2))</span></code>.</p>
<p>The average squared deviation is normally calculated as
<code class="docutils literal notranslate"><span class="pre">x.sum()</span> <span class="pre">/</span> <span class="pre">N</span></code>, where <code class="docutils literal notranslate"><span class="pre">N</span> <span class="pre">=</span> <span class="pre">len(x)</span></code>.  If, however, <em class="xref py py-obj">ddof</em> is
specified, the divisor <code class="docutils literal notranslate"><span class="pre">N</span> <span class="pre">-</span> <span class="pre">ddof</span></code> is used instead. In standard
statistical practice, <code class="docutils literal notranslate"><span class="pre">ddof=1</span></code> provides an unbiased estimator of the
variance of the infinite population. <code class="docutils literal notranslate"><span class="pre">ddof=0</span></code> provides a maximum
likelihood estimate of the variance for normally distributed variables.
The standard deviation computed in this function is the square root of
the estimated variance, so even with <code class="docutils literal notranslate"><span class="pre">ddof=1</span></code>, it will not be an
unbiased estimate of the standard deviation per se.</p>
<p>Note that, for complex numbers, <a class="reference internal" href="numpy.std.html#numpy.std" title="numpy.std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">std</span></code></a> takes the absolute value before
squaring, so that the result is always real and nonnegative.</p>
<p>For floating-point input, the <em>std</em> is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32 (see example
below).  Specifying a higher-accuracy accumulator using the <a class="reference internal" href="numpy.dtype.html#numpy.dtype" title="numpy.dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></a>
keyword can alleviate this issue.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">1.247219128924647</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([1., 0.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([0.,  0.5]) # may vary</span>
</pre></div>
</div>
</dd></dl>

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